In (REF ), \(\pi (\theta )\)  is the probability density function of the prior distribution for the calibration parameters, and \(\exp \left( - n \ell (\theta ; \mathbf {y}) \right)\)  is known as the generalised likelihood. [1]} show that the generalised posterior distribution provides a coherent representation of subjective uncertainty about the values of the calibration parameters that minimise the expectation of the loss with respect to the true data-generating process of the responses.
